AI-Driven Behavior Planning for Digital Mental Health Interventions in Youth

Wei Liu^*

• Sichuan Provincial Maternity and Child Health Care Hospital, Changchun, China

AI-driven behavior planning holds significant potential to transform digital mental health interventions for youth by delivering personalized, adaptive, and scalable therapeutic strategies. Traditional mental health interventions often rely on static rule-based models or self-reported assessments, which may lack real-time adaptability and fail to capture the complex and evolving nature of youth mental health dynamics. existing digital interventions frequently struggle with challenges such as low engagement, poor long-term adherence, and difficulties in integrating multimodal behavioral data, limiting their overall effectiveness. To address these pressing issues, we propose a deep reinforcement learning-based framework that leverages diverse multimodal user data—including textual inputs, physiological signals, and behavioral patterns—to dynamically optimize intervention strategies. Our approach integrates contextual bandits with deep reinforcement learning to tailor interventions based on user state trajectories, ensuring that each recommendation is not only personalized but also balances therapeutic effectiveness with sustained user engagement. we introduce an uncertainty-aware decision-making mechanism that quantifies the reliability of interventions, offering interpretable AI-assisted recommendations to clinicians. Through experimental evaluations on real-world mental health datasets, our framework demonstrates significant improvements in intervention effectiveness, user engagement, and adherence rates compared to conventional digital interventions. These findings underscore the transformative potential of AI-driven behavior planning in digital mental health care for youth, paving the way for more responsive, adaptive, and evidence-based therapeutic solutions.